Automated Mandarin tone classification using deep neural networks trained on a large speech dataset

Abstract

A tonal language is one in which the speaker’s intonation modifies the meaning of a word. In this work, we perform a rigorous analysis of intonation changes, or pitch contours, produced by native Mandarin speakers to predict the tone-contour type. Pitch contours are estimated using a number of different methods, also measuring each contour’s Mel-Frequency Cepstral Coefficients (MFCCs). The dataset used was autonomously generated from the Aishell open-source Mandarin speech corpus. Each sample was aligned with its transcript using Montreal Forced Alignment and segmented into individual words. The resulting corpus covers 11 topic domains, spoken by 400 individuals. Separate development, training, and testing datasets are created to ensure the integrity of our results. Pitch contours and their MFCCs are exposed to a number of machine learning techniques including clustered, regression, and traditional Deep Neural Network (DNN) approaches. MFCCs are additionally processed using convolutional neural networks. The models are used to predict the corresponding tone for a contour. Our work seeks to determine which intonation representations perform optimally for machine learning tasks. The tool is used to provide audio and visual feedback to learners of tonal languages. [Work supported by RPI Seed Grant and CISL].